npj Materials Sustainability,
Journal Year:
2024,
Volume and Issue:
2(1)
Published: April 8, 2024
Abstract
Data-driven
modeling
is
being
increasingly
applied
in
designing
and
optimizing
organic
waste
management
toward
greater
resource
circularity.
This
study
investigates
a
spectrum
of
data-driven
techniques
for
treatment,
encompassing
neural
networks,
support
vector
machines,
decision
trees,
random
forests,
Gaussian
process
regression,
k
-nearest
neighbors.
The
application
these
explored
terms
their
capacity
complex
processes.
Additionally,
the
delves
into
physics-informed
highlighting
significance
integrating
domain
knowledge
improved
model
consistency.
Comparative
analyses
are
carried
out
to
provide
insights
strengths
weaknesses
each
technique,
aiding
practitioners
selecting
appropriate
models
diverse
applications.
Transfer
learning
specialized
network
variants
also
discussed,
offering
avenues
enhancing
predictive
capabilities.
work
contributes
valuable
field
modeling,
emphasizing
importance
understanding
nuances
technique
informed
decision-making
various
treatment
scenarios.
Environmental Technology & Innovation,
Journal Year:
2023,
Volume and Issue:
32, P. 103341 - 103341
Published: Aug. 21, 2023
To
address
the
low
conversion
of
effective
phosphorus
during
previous
studies
on
spent
mushroom
substrate
(SMS)
composting,
phosphorus-solubilizing
bacteria
(PSB)
were
utilized
to
increase
content
in
this
study.
The
results
demonstrated
that
PSB
treatments
exhibited
higher
temperature
levels
up
66
°C.
TN,
NH4+-N,
and
NO3−-N
contents
than
those
control
treatment
(CK)
by
9.01%,
50.01%,
4.61%,
respectively.
Inoculation
with
increased
phosphorus,
total
humus
SMS
compost
6.84%,
11.05%,
9.10%.
In
addition,
based
PICRUSt
analysis,
inoculation
significantly
promoted
metabolic
pathways
associated
or
production
substances
can
facilitate
leaching,
thus
improving
utilization
compost.
conclusion,
addition
improve
bioavailability
P
realize
green
sustainable
development
edible
industry.
Circular Economy,
Journal Year:
2024,
Volume and Issue:
3(2), P. 100088 - 100088
Published: May 31, 2024
Biological
treatment
technologies
(such
as
anaerobic
digestion,
composting,
and
insect
farming)
have
been
extensively
employed
to
handle
various
degradable
organic
wastes.
However,
the
inherent
complexity
instability
of
biological
processes
adversely
affect
production
renewable
energy
nutrient-rich
products.
To
ensure
stable
consistent
product
quality,
researchers
invested
heavily
in
control
strategies
for
treatment,
with
machine
learning
(ML)
recently
proving
effective
optimizing
predicting
parameters,
detecting
disturbances,
enabling
real-time
monitoring.
This
review
critically
assesses
application
ML
providing
an
in-depth
evaluation
key
algorithms.
study
reveals
that
artificial
neural
networks,
tree-based
models,
support
vector
machines,
genetic
algorithms
are
leading
treatment.
A
thorough
investigation
applications
farming
underscores
its
remarkable
capacity
predict
products,
optimize
processes,
perform
monitoring,
mitigate
pollution
emissions.
Furthermore,
this
outlines
challenges
prospects
encountered
applying
highlighting
crucial
directions
future
research
area.
